Name
Abstract | ||
|---|---|---|
Gait recognition can be performed without subject cooperation under harsh conditions, thus it is an important tool in forensic gait analysis, security control, and other commercial applications. One critical issue that prevents gait recognition systems from being widely accepted is the performance drop when the camera viewpoint varies between the registered templates and the query data. In this paper, we explore the potential of combining feature optimisers and representations learned by convolutional neural networks (CNN) to achieve efficient view-invariant gait recognition. The experimental results indicate that CNN learns highly discriminative representations across moderate view variations, and these representations can be further improved using view-invariant feature selectors, achieving a high matching accuracy across views. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/BTAS.2017.8272769 | 2017 IEEE International Joint Conference on Biometrics (IJCB) |
Keywords | Field | DocType |
security control,commercial applications,optimised representations learning,view-invariant gait recognition,camera viewpoint,CNN,forensic gait analysis,view-invariant feature selectors,moderate view variations,discriminative representations,convolutional neural networks,feature optimisers,query data,performance drop,gait recognition systems | Economics,Security controls,Gait,Pattern recognition,Convolutional neural network,Gait analysis,Solid modeling,Invariant (mathematics),Artificial intelligence,Finance,Discriminative model | Conference |
ISBN | Citations | PageRank |
978-1-5386-1125-8 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ning Jia | 1 | 2 | 1.40 |
| Victor Sanchez | 2 | 144 | 31.22 |
| Chang-Tsun Li | 3 | 24 | 5.11 |